title: OSA severity assessment based on sleep breathing analysis using ambient microphone.
abstract: In this paper, an audio-based system for severity estimation of obstructive sleep apnea (OSA) is proposed. The system estimates the apnea-hypopnea index (AHI), which is the average number of apneic events per hour of sleep. This system is based on a Gaussian mixture regression algorithm that was trained and validated on full-night audio recordings. Feature selection process using a genetic algorithm was applied to select the best features extracted from time and spectra domains. A total of 155 subjects, referred to in-laboratory polysomnography (PSG) study, were recruited. Using the PSG's AHI score as a gold-standard, the performances of the proposed system were evaluated using a Pearson correlation, AHI error, and diagnostic agreement methods. Correlation of R=0.89, AHI error of 7.35 events/hr, and diagnostic agreement of 77.3% were achieved, showing encouraging performances and a reliable non-contact alternative method for OSA severity estimation.
authors.forename: E
authors.initials: E
authors.lastname: Dafna
authors.forename: A
authors.initials: A
authors.lastname: Tarasiuk
authors.forename: Y
authors.initials: Y
authors.lastname: Zigel
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001049:Apnea / Q000503:physiopathology; D015992:Body Mass Index; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods; D012119:Respiration; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*; D012913:Snoring / Q000175:diagnosis; D055815:Young Adult